import numpy as np
from scipy.signal import kalman_filter
import time

class QuantumFilterService:
    """
    量子-经典混合滤波服务
    结合量子退火预处理和经典卡尔曼滤波精修
    包含降级机制以应对量子计算硬件限制
    """
    def __init__(self, use_quantum_hardware=False, fallback_to_classical=False):
        self.use_quantum_hardware = use_quantum_hardware
        self.fallback_to_classical = fallback_to_classical
        
        # 量子退火器模拟 (实际中会连接D-Wave硬件)
        self.annealer_available = True
        self.quantum_processing_time = []
        self.classical_processing_time = []
        
        # 经典卡尔曼滤波器
        self.kalman = self._init_kalman_filter()
        
        # 脉冲噪声处理阈值
        self.pulse_noise_threshold = 0.15
        
        # 性能指标
        self.performance_metrics = {
            'quantum_used': 0,
            'classical_fallback': 0,
            'filter_accuracy': [],
            'processing_latency': []
        }
        
        # 量子硬件状态监测
        self.quantum_temperature = 15  # mK，初始值
        self.temperature_threshold = 15  # mK，超过此值触发降级
        
    def _init_kalman_filter(self):
        """初始化卡尔曼滤波器参数"""
        # 简化的卡尔曼滤波器实现
        class SimpleKalmanFilter:
            def __init__(self):
                self.x = None  # 状态估计
                self.P = None  # 估计误差协方差
                self.Q = 0.01  # 过程噪声协方差
                self.R = 0.1   # 测量噪声协方差
                
            def update(self, z):
                """更新状态估计"""
                if self.x is None:
                    self.x = z
                    self.P = np.eye(len(z))
                    return self.x
                    
                # 预测步骤
                x_pred = self.x
                P_pred = self.P + self.Q
                
                # 更新步骤
                K = P_pred / (P_pred + self.R)  # 卡尔曼增益
                self.x = x_pred + K * (z - x_pred)
                self.P = (1 - K) * P_pred
                
                return self.x
                
            def refine(self, signal):
                """精修信号序列"""
                result = []
                for s in signal:
                    result.append(self.update(s))
                return np.array(result)
                
        return SimpleKalmanFilter()
    
    def _check_quantum_health(self):
        """检查量子硬件健康状态"""
        # 模拟温度波动
        self.quantum_temperature += np.random.normal(0, 2)
        self.quantum_temperature = max(10, min(20, self.quantum_temperature))
        
        # 检查温度是否在允许范围内
        if self.quantum_temperature > self.temperature_threshold:
            self.annealer_available = False
            return False
        
        # 模拟其他可能的故障
        if np.random.random() < 0.05:  # 5% 概率模拟故障
            self.annealer_available = False
            return False
            
        self.annealer_available = True
        return True
    
    def _quantum_annealing(self, signal):
        """
        量子退火预处理
        实际实现中会调用D-Wave量子退火器
        此处为模拟实现
        """
        start_time = time.time()
        
        # 模拟量子退火处理时间
        time.sleep(0.01)  # 模拟量子处理延迟
        
        # 量子退火模拟：寻找能量最低状态
        # 简化实现：减少信号噪声，保留重要特征
        processed = []
        for s in signal:
            # 应用脉冲噪声过滤
            if abs(s) > self.pulse_noise_threshold:
                # 对于脉冲噪声，应用更强的过滤
                processed.append(s * 0.7)
            else:
                # 对于普通噪声，轻微过滤
                processed.append(s * 0.9 + np.random.normal(0, 0.02))
        
        processing_time = time.time() - start_time
        self.quantum_processing_time.append(processing_time)
        
        return np.array(processed)
    
    def _classical_filter(self, signal):
        """纯经典滤波作为降级方案"""
        start_time = time.time()
        
        # 应用中值滤波处理脉冲噪声
        filtered = []
        window = []
        for s in signal:
            window.append(s)
            if len(window) > 5:
                window.pop(0)
            # 中值滤波减少脉冲噪声
            filtered.append(np.median(window))
        
        processing_time = time.time() - start_time
        self.classical_processing_time.append(processing_time)
        
        return np.array(filtered)
    
    @property
    def fpga_accelerated(self):
        """
        FPGA加速装饰器模拟
        实际实现中会调用硬件加速接口
        """
        def decorator(func):
            def wrapper(*args, **kwargs):
                # 模拟FPGA加速效果 - 减少处理时间
                start_time = time.time()
                result = func(*args, **kwargs)
                processing_time = time.time() - start_time
                
                # 记录性能指标
                self.performance_metrics['processing_latency'].append(processing_time)
                return result
            return wrapper
        return decorator
    
    @fpga_accelerated
    def filter(self, signal, context=None):
        """
        主滤波函数：结合量子-经典混合滤波
        包含自动降级机制应对量子硬件问题
        """
        # 确保输入是numpy数组
        signal = np.array(signal)
        
        # 检查是否可以使用量子处理
        use_quantum = self.use_quantum_hardware and self._check_quantum_health()
        
        try:
            if use_quantum:
                # 量子预处理
                quantum_result = self._quantum_annealing(signal)
                # 经典精修
                final_result = self.kalman.refine(quantum_result)
                self.performance_metrics['quantum_used'] += 1
            else:
                # 纯经典处理作为降级方案
                classical_result = self._classical_filter(signal)
                final_result = self.kalman.refine(classical_result)
                self.performance_metrics['classical_fallback'] += 1
                
            # 计算滤波精度（与原始信号的差异）
            # 注意：实际应用中需要参考信号的真实值
            accuracy = 1 - np.mean(np.abs(final_result - signal))
            self.performance_metrics['filter_accuracy'].append(accuracy)
            
            return final_result
            
        except Exception as e:
            # 发生任何错误时降级到经典滤波
            print(f"滤波处理出错: {str(e)}, 降级到经典滤波")
            classical_result = self._classical_filter(signal)
            final_result = self.kalman.refine(classical_result)
            self.performance_metrics['classical_fallback'] += 1
            return final_result
    
    def get_performance_metrics(self):
        """返回滤波服务的性能指标"""
        return {
            'total_processed': self.performance_metrics['quantum_used'] + 
                              self.performance_metrics['classical_fallback'],
            'quantum_usage_ratio': self.performance_metrics['quantum_used'] / 
                                 (self.performance_metrics['quantum_used'] + 
                                  self.performance_metrics['classical_fallback'] + 1e-8),
            'avg_accuracy': np.mean(self.performance_metrics['filter_accuracy']) 
                          if self.performance_metrics['filter_accuracy'] else 0,
            'avg_latency': np.mean(self.performance_metrics['processing_latency']) 
                         if self.performance_metrics['processing_latency'] else 0,
            'last_quantum_temp': self.quantum_temperature,
            'annealer_available': self.annealer_available
        }
    